Roadmap for the evolution of monitoring: developing and evaluating waveform-based variability-derived artificial intelligence-powered predictive clinical decision support software tools

被引：2

作者：

Seely, Andrew J. E. ^{[1
,2
,3
]}

Newman, Kimberley ^{[2
]}

Ramchandani, Rashi ^{[1
]}

Herry, Christophe ^{[2
]}

Scales, Nathan ^{[2
]}

Hudek, Natasha ^{[1
,2
]}

Brehaut, Jamie ^{[2
]}

Jones, Daniel ^{[1
]}

Ramsay, Tim ^{[2
]}

Barnaby, Doug ^{[4
]}

Fernando, Shannon ^{[5
]}

Perry, Jeffrey ^{[6
]}

Dhanani, Sonny ^{[7
]}

Burns, Karen E. A. ^{[8
,9
,10
]}

机构：

[1] Univ Ottawa, Fac Med Ottawa, Ottawa, ON, Canada

[2] Ottawa Hosp, Res Inst, Ottawa, ON, Canada

[3] Ottawa Hosp, Dept Crit Care, Gen Campus,501 Smyth Rd,Box 708, Ottawa, ON K1H 8L6, Canada

[4] Albert Einstein Coll Med, Dept Emergency Med, Bronx, NY USA

[5] Lakeridge Hosp, Dept Emergency Med, Oshawa, ON, Canada

[6] Univ Ottawa, Dept Emergency Med, Ottawa, ON, Canada

[7] Childrens Hosp Eastern Ontario, Crit Care, Ottawa, ON, Canada

[8] Univ Toronto, Interdept Div Crit Care, Toronto, ON, Canada

[9] Unity Hlth Toronto, Div Crit Care Med, Dept Med, St Michaels Hosp, Toronto, ON, Canada

[10] St Michaels Hosp, Li Ka Shing Knowledge Inst, Toronto, ON, Canada

来源：

CRITICAL CARE | 2024年 / 28卷 / 01期

基金：

加拿大健康研究院;

关键词：

Clinical decision support; Artificial intelligence; Waveform monitoring; Variability analysis; Heart rate variability; Respiratory rate variability; Critical illness; HEART-RATE-VARIABILITY; ORGAN DYSFUNCTION SYNDROME; INTENSIVE-CARE-UNIT; EXTUBATION FAILURE; SEVERE SEPSIS; PHYSIOLOGICAL COMPLEXITY; HOSPITAL MORTALITY; SPECTRAL-ANALYSIS; DIAGNOSTIC ERROR; OUTCOMES;

D O I：

10.1186/s13054-024-05140-6

中图分类号：

R4 [临床医学];

学科分类号：

1002 ; 100602 ;

摘要：

Background Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring. Methods We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring. Results Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial). Conclusions The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care.

引用

页数：10

共 65 条

[1] Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care [J].